Emotion recognition is an important field of research in Brain Computer Interactions. As technology and the understanding of emotions are advancing, there are growing opportunities for automatic emotion recognition systems. Neural networks are a family of statistical learning models inspired by biological neural networks and are used to estimate functions that can depend on a large number of inputs that are generally unknown. In this paper we seek to use this effectiveness of Neural Networks to classify user emotions using EEG signals from the DEAP (Koelstra et al (2012)) dataset which represents the benchmark for Emotion classification research. We explore 2 different Neural Models, a simple Deep Neural Network and a Convolutional Neural Network for classification. Our model provides the state-of-the-art classification accuracy, obtaining 4.51 and 4.96 percentage point improvements over (Rozgic et al (2013)) classification of Valence and Arousal into 2 classes (High and Low) and 13.39 and 6.58 percentage point improvements over (Chung and Yoon(2012)) classification of Valence and Arousal into 3 classes (High, Normal and Low). Moreover our research is a testament that Neural Networks could be robust classifiers for brain signals, even outperforming traditional learning techniques.
Liquefaction of Citrus limetta fruit waste biomass for efficient waste management. Higher yield of biocrude obtained compared to relevant works on citrus fruit wastes.
BACKGROUND: Liquefaction of biomass has an advantage over other thermochemical processes of conversion and its co-liquefaction aims to overcome the restrictions and limitations imposed upon single feedstock liquefaction. Valorization of Citrus limetta peel and pulp wastes by their co-liquefaction in a hydrogen-donor solvent such as methanol was chosen as an objective herein for efficient conversion of raw materials to obtain better quality biocrude.RESULTS: A maximum biocrude yield of 11.25 wt. % was obtained from solvothermal co-liquefaction of Citrus limetta peel and pulp biomasses at 260 °C temperature, with 30 min residence time, at three different ratios of biomass to solvent (1:2, 1:3 and 1:4). This was higher than the biocrude yield from liquefaction of individual C. limetta pulp biomass as well as other studies on similar citrus fruit wastes. The higher heating value (HHV) of biocrude was raised to 25.72 MJ kg −1 at 1:3 biomass:solvent ratio by co-liquefaction, showing its synergy over individual C. limetta peel biocrude. The presence of esters, ketones, alcohols, hydrocarbons and fatty acids in the biocrude product were found from Gas Chromatography-Mass Spectrometry (GC-MS) results. The biochar obtained was mesoporous in nature and could be employed for adsorption in bioremediation of water and soil.CONCLUSION: Co-liquefaction of homogeneous lignocellulosic biomasses have the ability to boost production and the quality of the biocrude and biochar attained in comparison to products of individual liquefaction. Future studies on upgrading the quality of products are required.
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